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更新时间：2024-05-10 GMT+08:00
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Tensorflow训练

Kubeflow部署成功后，使用ps-worker的模式来进行Tensorflow训练就变得非常容易。本节介绍一个Kubeflow官方的Tensorflow训练范例，您可参考TensorFlow Training (TFJob)获取更详细的信息。

创建MNIST示例

  1. 部署TFJob资源以开始训练。

    创建tf-mnist.yaml文件，示例如下： 
    apiVersion: "kubeflow.org/v1"
kind: TFJob
metadata:
  name: tfjob-simple
  namespace: kubeflow
spec:
  tfReplicaSpecs:
    Worker:
      replicas: 2
      restartPolicy: OnFailure
      template:
        spec:
          containers:
            - name: tensorflow
              image: kubeflow/tf-mnist-with-summaries:latest
              command:
                - "python"
                - "/var/tf_mnist/mnist_with_summaries.py"

  2. 创建TFJob。 

    kubectl apply -f tf-mnist.yaml

  3. 等待worker运行完毕后，查看运行日志。 

    kubectl -n kubeflow logs tfjob-simple-worker-0

    回显如下：

    ...
Accuracy at step 900: 0.964
Accuracy at step 910: 0.9653
Accuracy at step 920: 0.9665
Accuracy at step 930: 0.9681
Accuracy at step 940: 0.9664
Accuracy at step 950: 0.9667
Accuracy at step 960: 0.9694
Accuracy at step 970: 0.9683
Accuracy at step 980: 0.9687
Accuracy at step 990: 0.966
Adding run metadata for 999

  4. 删除TFJob。 

    kubectl delete -f tf-mnist.yaml

使用GPU训练

TFJob可在GPU场景下进行，该场景需要集群中包含GPU节点，并安装合适的驱动。

  1. 在TFJob中指定GPU资源。

    创建tf-gpu.yaml文件，示例如下：

    该示例的主要功能是基于Tensorflow的分布式架构，利用卷积神经网络（CNN）中的ResNet50模型对随机生成的图像进行训练，每次训练32张图像（batch_size），共训练100次（step），记录每次训练过程中的性能（image/sec）。

    apiVersion: "kubeflow.org/v1"
kind: "TFJob"
metadata:
  name: "tf-smoke-gpu"
spec:
  tfReplicaSpecs:
    PS:
      replicas: 1
      template:
        metadata:
          creationTimestamp: null
        spec:
          containers:
            - args:
                - python
                - tf_cnn_benchmarks.py
                - --batch_size=32
                - --model=resnet50
                - --variable_update=parameter_server
                - --flush_stdout=true
                - --num_gpus=1
                - --local_parameter_device=cpu
                - --device=cpu
                - --data_format=NHWC
              image: docker.io/kubeflow/tf-benchmarks-cpu:v20171202-bdab599-dirty-284af3
              name: tensorflow
              ports:
                - containerPort: 2222
                  name: tfjob-port
              resources:
                limits:
                  cpu: "1"
              workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure
    Worker:
      replicas: 1
      template:
        metadata:
          creationTimestamp: null
        spec:
          containers:
            - args:
                - python
                - tf_cnn_benchmarks.py
                - --batch_size=32
                - --model=resnet50
                - --variable_update=parameter_server
                - --flush_stdout=true
                - --num_gpus=1
                - --local_parameter_device=cpu
                - --device=gpu
                - --data_format=NHWC
              image: docker.io/kubeflow/tf-benchmarks-gpu:v20171202-bdab599-dirty-284af3
              name: tensorflow
              ports:
                - containerPort: 2222
                  name: tfjob-port
              resources:
                limits:
                  nvidia.com/gpu: 1       # GPU数量
              workingDir: /opt/tf-benchmarks/scripts/tf_cnn_benchmarks
          restartPolicy: OnFailure

  2. 创建TFJob。 

    kubectl apply -f tf-gpu.yaml

  3. 等待worker运行完毕后（一般GPU训练大约需要5分钟），执行如下命令查看运行结果： 

    kubectl logs tf-smoke-gpu-worker-0

    回显如下：

    ...
INFO|2023-09-02T12:04:25|/opt/launcher.py|27| Running warm up
INFO|2023-09-02T12:08:55|/opt/launcher.py|27| Done warm up
INFO|2023-09-02T12:08:55|/opt/launcher.py|27| Step      Img/sec loss
INFO|2023-09-02T12:08:56|/opt/launcher.py|27| 1 images/sec: 68.8 +/- 0.0 (jitter = 0.0) 8.777
INFO|2023-09-02T12:09:00|/opt/launcher.py|27| 10        images/sec: 70.4 +/- 0.4 (jitter = 1.8) 8.557
INFO|2023-09-02T12:09:04|/opt/launcher.py|27| 20        images/sec: 70.5 +/- 0.3 (jitter = 1.5) 8.090
INFO|2023-09-02T12:09:09|/opt/launcher.py|27| 30        images/sec: 70.3 +/- 0.3 (jitter = 1.6) 8.041
INFO|2023-09-02T12:09:13|/opt/launcher.py|27| 40        images/sec: 70.1 +/- 0.2 (jitter = 1.7) 9.464
INFO|2023-09-02T12:09:18|/opt/launcher.py|27| 50        images/sec: 70.1 +/- 0.2 (jitter = 1.6) 7.797
INFO|2023-09-02T12:09:23|/opt/launcher.py|27| 60        images/sec: 70.1 +/- 0.2 (jitter = 1.6) 8.595
INFO|2023-09-02T12:09:27|/opt/launcher.py|27| 70        images/sec: 70.0 +/- 0.2 (jitter = 1.7) 7.853
INFO|2023-09-02T12:09:32|/opt/launcher.py|27| 80        images/sec: 69.9 +/- 0.2 (jitter = 1.7) 7.849
INFO|2023-09-02T12:09:36|/opt/launcher.py|27| 90        images/sec: 69.8 +/- 0.2 (jitter = 1.7) 7.911
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| 100       images/sec: 69.7 +/- 0.1 (jitter = 1.7) 7.853
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| ----------------------------------------------------------------
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| total images/sec: 69.68
INFO|2023-09-02T12:09:41|/opt/launcher.py|27| ----------------------------------------------------------------
INFO|2023-09-02T12:09:42|/opt/launcher.py|80| Finished: python tf_cnn_benchmarks.py --batch_size=32 --model=resnet50 --variable_update=parameter_server --flush_stdout=true --num_gpus=1 --local_parameter_device=cpu --device=gpu --data_format=NHWC --job_name=worker --ps_hosts=tf-smoke-gpu-ps-0.default.svc:2222 --worker_hosts=tf-smoke-gpu-worker-0.default.svc:2222 --task_index=0
INFO|2023-09-02T12:09:42|/opt/launcher.py|84| Command ran successfully sleep for ever.

    可以看到单个GPU的训练性能为69.68 images/sec。

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